import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str = None   ,   lowerCamelCase_						:						uuid.UUID = None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Tuple=None				):





    """simple docstring"""


    if not conversation_id:
     UpperCamelCase			  =      uuid.uuida()
    if past_user_inputs is None:
     UpperCamelCase			  =      []
    if generated_responses is None:
     UpperCamelCase			  =      []

    UpperCamelCase			  =      conversation_id
    UpperCamelCase			  =      past_user_inputs
    UpperCamelCase			  =      generated_responses
    UpperCamelCase			  =      text







   def __eq__(     self						:						str   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     return False
    if self.uuid == other.uuid:
     return True
    return (
        self.new_user_input == other.new_user_input
        and self.past_user_inputs == other.past_user_inputs
        and self.generated_responses == other.generated_responses
    )







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						bool = False				):





    """simple docstring"""


    if self.new_user_input:
     if overwrite:
      logger.warning(
          f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
          f"""with: \"{text}\"."""				)
      UpperCamelCase			  =      text
     else:
      logger.warning(
          f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
          f"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""				)
    else:
     UpperCamelCase			  =      text







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    if self.new_user_input:
     self.past_user_inputs.append(self.new_user_input				)
    UpperCamelCase			  =      None







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    self.generated_responses.append(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    for user_input, generated_response in zip(self.past_user_inputs   ,   self.generated_responses				):
     yield True, user_input
     yield False, generated_response
    if self.new_user_input:
     yield True, self.new_user_input







   def __repr__(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      f"""Conversation id: {self.uuid} \n"""
    for is_user, text in self.iter_texts():
     UpperCamelCase			  =      """user""" if is_user else """bot"""
     output += f"""{name} >> {text} \n"""
    return output





@add_end_docstrings(
    __lowerCAmelCase  ,     r"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """  ,     )
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def __init__(     self						:						Tuple   ,   *lowerCamelCase_						:						List[Any]   ,   **lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__(*lowerCamelCase_   ,   **lowerCamelCase_				)
    if self.tokenizer.pad_token_id is None:
     UpperCamelCase			  =      self.tokenizer.eos_token







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Optional[Any]=None   ,   lowerCamelCase_						:						Tuple=None   ,   **lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      {}
    UpperCamelCase			  =      {}
    UpperCamelCase			  =      {}

    if min_length_for_response is not None:
     UpperCamelCase			  =      min_length_for_response
    if minimum_tokens is not None:
     UpperCamelCase			  =      minimum_tokens

    if "max_length" in generate_kwargs:
     UpperCamelCase			  =      generate_kwargs["""max_length"""]
     # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
    if clean_up_tokenization_spaces is not None:
     UpperCamelCase			  =      clean_up_tokenization_spaces

    if generate_kwargs:
     forward_params.update(lowerCamelCase_				)
    return preprocess_params, forward_params, postprocess_params







   def __call__(     self						:						Tuple   ,   lowerCamelCase_						:						Union[Conversation, List[Conversation]]   ,   lowerCamelCase_						:						Union[str, Any]=0   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      super().__call__(lowerCamelCase_   ,   num_workers=lowerCamelCase_   ,   **lowerCamelCase_				)
    if isinstance(lowerCamelCase_   ,   lowerCamelCase_				) and len(lowerCamelCase_				) == 1:
     return outputs[0]
    return outputs







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Conversation   ,   lowerCamelCase_						:						Any=32				):





    """simple docstring"""


    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""				)
    if conversation.new_user_input is None:
     raise ValueError(
         f"""Conversation with UUID {type(conversation.uuid				)} does not contain new user input to process. """
         """Add user inputs with the conversation's `add_user_input` method"""				)
    if hasattr(self.tokenizer   ,   """_build_conversation_input_ids"""				):
     UpperCamelCase			  =      self.tokenizer._build_conversation_input_ids(lowerCamelCase_				)
    else:
     # If the tokenizer cannot handle conversations, we default to only the old version
     UpperCamelCase			  =      self._legacy_parse_and_tokenize(lowerCamelCase_				)

    if self.framework == "pt":
     UpperCamelCase			  =      torch.LongTensor([input_ids]				)
    elif self.framework == "tf":
     UpperCamelCase			  =      tf.constant([input_ids]				)
    return {"input_ids": input_ids, "conversation": conversation}







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Tuple=10   ,   **lowerCamelCase_						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      generate_kwargs.get("""max_length"""   ,   self.model.config.max_length				)

    UpperCamelCase			  =      model_inputs["""input_ids"""].shape[1]
    if max_length - minimum_tokens < n:
     logger.warning(f"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""				)
     UpperCamelCase			  =      max_length - minimum_tokens
     UpperCamelCase			  =      model_inputs["""input_ids"""][:, -trim:]
     if "attention_mask" in model_inputs:
      UpperCamelCase			  =      model_inputs["""attention_mask"""][:, -trim:]
    UpperCamelCase			  =      model_inputs.pop("""conversation"""				)
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      self.model.generate(**lowerCamelCase_   ,   **lowerCamelCase_				)
    if self.model.config.is_encoder_decoder:
     UpperCamelCase			  =      1
    else:
     UpperCamelCase			  =      n
    return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						List[str]=True				):





    """simple docstring"""


    UpperCamelCase			  =      model_outputs["""output_ids"""]
    UpperCamelCase			  =      self.tokenizer.decode(
        output_ids[0]   ,   skip_special_tokens=lowerCamelCase_   ,   clean_up_tokenization_spaces=lowerCamelCase_   ,   )
    UpperCamelCase			  =      model_outputs["""conversation"""]
    conversation.mark_processed()
    conversation.append_response(lowerCamelCase_				)
    return conversation







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Conversation				):





    """simple docstring"""


    UpperCamelCase			  =      self.tokenizer.eos_token_id
    UpperCamelCase			  =      []
    for is_user, text in conversation.iter_texts():
     if eos_token_id is not None:
      input_ids.extend(self.tokenizer.encode(lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_				) + [eos_token_id]				)
     else:
      input_ids.extend(self.tokenizer.encode(lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_				)				)

    if len(lowerCamelCase_				) > self.tokenizer.model_max_length:
     UpperCamelCase			  =      input_ids[-self.tokenizer.model_max_length :]
    return input_ids

import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
     import jax
@require_flax
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				FlaxAutoencoderKL







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      4
    UpperCamelCase			  =      3
    UpperCamelCase			  =      (32, 32)

    UpperCamelCase			  =      jax.random.PRNGKey(0				)
    UpperCamelCase			  =      jax.random.uniform(lowerCamelCase_   ,   ((batch_size, num_channels) + sizes)				)

    return {"sample": image, "prng_key": prng_key}







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      {
        """block_out_channels""": [32, 64],
        """in_channels""": 3,
        """out_channels""": 3,
        """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
        """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
        """latent_channels""": 4,
    }
    UpperCamelCase			  =      self.dummy_input
    return init_dict, inputs_dict
